Strategic advice & alerts to leverage data analytics & new technologies
Leverage data and the technologies that generate it, from IoT to AI/machine learning, wearables, blockchain, and more, to improve decision-making, enrich collaboration and enable new services.
Adapting to the changing business environment of a digital business is about much more than implementing new technologies like analytics, the IoT, and so on. Rather, business managers from the boardroom down must drive the adoption and skilled use of data-based decision making. Ultimately, middle managers are critical to digital business — making data-based decisions and selecting tasks that provide the necessary capabilities to deliver on a digital transformation vision and strategy.
Starting from a data warehouse just makes sense. Of course, the architectural thinking and technology offer valuable intellectual capital to IT. But the real value comes from the decades of experience in information/data governance and management, as well as the interpersonal and organizational skills that DW implementers have gathered. As you will see from the articles in this issue, the contributors are on the same path.
Artificial intelligence (AI) is moving into public sector agencies — at both the state and federal levels — for use in such general applications as compliance, records management, community development, and finance, among others. Governments are also applying, or researching the use of, AI for other applications, including optimizing access to services and analyzing and predicting the likelihood of environmental disasters. This Advisor provides some examples of some of the many government research efforts underway that are intended to advance AI’s capabilities beyond its current limitations.
An awareness of granularity and context in analytics is vital for creating value to the business. In this Advisor, I focus on understanding the degree of granularity of analysis and how organizations can incorporate granularity in their analytical solutions.
Cutter Consortium is conducting a survey on how organizations are adopting, or planning to adopt, artificial intelligence (AI) technologies. We also seek to identify important issues and other considerations they are encountering or foresee encountering in their efforts. In addition to gathering and analyzing survey data, I have been interviewing leaders and practitioners from different organizations implementing or working to implement AI applications. Here, in Part VI, we jump right into where we left off and continue examining industries that AI is expected to disrupt.
Companies are turning to machine learning, computer vision, robotics, and other AI technologies to revitalize the retail shopping experience and boost customer experience and business benefits — both online and offline. But it is the new, cutting-edge, AI-driven applications under development that are most interesting — holding the promise of opening up new business models and possibly disrupting the retail sector.
This article gives us the complete context of what governance means, considering the data lifecycle (create, store, use, etc.) and the cognitive hierarchy of data, information, knowledge, understanding, and wisdom. The authors also look at the elements contained in a formal data model and what these elements tell us about the governance actions that need to be taken when data is accessed, modified, or deleted.
This article describes what good governance means for public sector institutions that are embracing open data initiatives. While these organizations make data accessible to increase government transparency and promote economic empowerment, they face additional responsibilities in terms of data quality, privacy compliance, security, and more.